Library
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(leaflet)
Sourced from: http://eric.clst.org/wupl/Stuff/gz_2010_us_050_00_5m.json http://eric.clst.org/wupl/Stuff/gz_2010_us_040_00_5m.json Need to cite US Census Beauro, see (http://eric.clst.org/Stuff/USGeoJSON) for more info All actual data sourced from NIOSH, need to find urls, etc. (add in dates / redownload for final)
uscounties <- rgdal::readOGR("data/counties_5m.json", "OGRGeoJSON")
## OGR data source with driver: GeoJSON
## Source: "data/counties_5m.json", layer: "OGRGeoJSON"
## with 3221 features
## It has 6 fields
usstates <- rgdal::readOGR("data/state_5m.json", "OGRGeoJSON")
## OGR data source with driver: GeoJSON
## Source: "data/state_5m.json", layer: "OGRGeoJSON"
## with 52 features
## It has 5 fields
Test leaflet map with counties included ##Leaflet Test
leaflet(uscounties) %>%
addTiles() %>%
addPolygons(data = uscounties, color = "blue", weight = 1, fillOpacity = 0) %>%
addPolygons(data = usstates, color = "black", weight = 5, fillOpacity = 0)
We will use rapidProgression_clean for these plots (only df that has county info built in) ##Data
rapidProg_raw <- read.csv("~/CWP-trends/data/rapidProgression_clean.csv")
rapidProg_raw$X <- NULL
uscounties$REGION <- NA
usstates$REGION <- NA
states <- as.data.frame(usstates$NAME)
states$STATE <- (usstates$STATE)
df_counties <- as.data.frame(uscounties)
df_join <- dplyr::left_join(df_counties, states, by = "STATE")
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y):
## '.Random.seed' is not an integer vector but of type 'NULL', so ignored
df_join <- within(df_join, REGION[`usstates$NAME` %in% c( 'Maryland'
, 'Pennsylvania'
, 'Ohio'
)
] <- 'Northern Appalachia')
df_join <- within(df_join, REGION[`usstates$NAME` == 'West Virginia' & NAME %in% c( "Barbour"
, "Brooke"
, "Clay"
, "Grant"
, "Greenbrier"
, "Harrison"
, "Lincoln"
, "Marion"
, "Marshall"
, "Monongalia"
, "Preston"
, "Raleigh"
, "Randolph"
, "Tucker"
, "Upshur"
, "Webster"
)
] <- 'Northern Appalachia'
)
df_join <- within(df_join, REGION[`usstates$NAME` %in% c( 'Illinois'
, 'Indiana'
)
] <- 'Mid-West')
df_join <- within(df_join, REGION[`usstates$NAME` == 'Kentucky' & NAME %in% c( "Hopkins"
, "Union"
, "Webster"
)
] <- 'Mid-West')
df_join <- within(df_join, REGION[`usstates$NAME` %in% c( 'Virginia'
, 'Tennessee'
)
] <- 'Central Appalachia')
df_join <- within(df_join, REGION[`usstates$NAME` == 'Kentucky' & NAME %in% c( "Bell"
, "Boyd"
, "Breathitt"
, "Christian"
, "Clay"
, "Daviess"
, "Estill"
, "Floyd"
, "Harlan"
, "Henderson"
, "Jackson"
, "Johnson"
, "Knott"
, "Knox"
, "Laurel"
, "Lawrence"
, "Leslie"
, "Letcher"
, "Martin"
, "Mclean"
, "Muhlenberg"
, "Perry"
, "Pike"
, "Whitley"
, "Wolfe"
)
] <- 'Central Appalachia')
df_join <- within(df_join, REGION[`usstates$NAME` == 'West Virginia' & NAME %in% c( "Boone"
, "Fayette"
, "Kanawha"
, "Logan"
, "McDowell"
, "Mingo"
, "Nicholas"
, "Wayne"
, "Wyoming"
)
] <- 'Central Appalachia')
df_join <- within(df_join, REGION[`usstates$NAME` %in% c( 'Alabama'
, 'Arkansas'
, 'Louisiana'
)
] <- 'Southern Appalachia')
df_join <- within(df_join, REGION[`usstates$NAME` %in% c( 'Arizona'
, 'Colorado'
, 'Montana'
, 'New Mexico'
, 'North Dakota'
, 'Oklahoma'
, 'Texas'
, 'Utah'
, 'Wyoming'
, 'Washington'
)
] <- 'West')
save(df_join, file = "data/serverData.RData")